Anish Athalye
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 9 papers; trusts 285 researchers and is trusted by 267 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 194 citations into it; #1,230 of 35,345 by trust here, #1,719 by citations
- Safety, Robustness & Interpretability — 169 citations into it; #46 of 2,251 by trust here, #78 by citations
- Computer Vision and Pattern Recognition — 71 citations into it; #6,168 of 21,575 by trust here, #9,563 by citations
- Signal Processing — 49 citations into it; #1,177 of 7,572 by trust here, #530 by citations
- Hardware and Architecture — 21 citations into it; #8 of 1,551 by trust here, #57 by citations
- Evaluation & Benchmarks — 14 citations into it
- Optimization & Training — 14 citations into it
- Model Architectures — 12 citations into it
Whom Anish Athalye trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nicholas Carlini
- Christian Szegedy
- Ian J. Goodfellow
- David A. Wagner
- Nicolas Papernot
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yoshua Bengio
Who trusts Anish Athalye overall
- Nicholas Carlini
- Eric Wong
- Florian Tramèr
- Tianyu Pang
- Logan Engstrom
- Yinpeng Dong
- Jonathan Uesato
- Lukas Schott
- Andrew Ilyas
- Sven Gowal
Papers from Anish Athalye's perspective
Ordered by how strongly the researchers Anish Athalye trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples (2018) — 3,560 citations
- Black-box Adversarial Attacks with Limited Queries and Information (2018) — 1,400 citations
- Query-Efficient Black-box Adversarial Examples (2017) — 53 citations
- On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses (2018) — 175 citations
- Evaluating and Understanding the Robustness of Adversarial Logit Pairing (2018) — 145 citations
- On Evaluating Adversarial Robustness (2019) — 1,007 citations
- Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks (2021) — 711 citations
- Synthesizing Robust Adversarial Examples (2017) — 95 citations
- Synthesizing Robust Adversarial Examples (2017) — 1,876 citations